Abstract

Bedrock outcrops are common on central Appalachian Mountain ridgelines. Because these ridgelines define watersheds, the rate at which they erode influences the pace of landscape evolution. To estimate ridgeline erosion rates, we sampled 72 quartz-bearing outcrops from the Potomac and Susquehanna River Basins and measured in situ–produced 10Be. Ridgeline erosion rates average 9 ± 1 m m.y.−1 (median = 6 m m.y.−1), similar to 10Be-derived rates previously reported for the region. The range of erosion rates we calculated reflects the wide distribution of samples we collected and the likely inclusion of outcrops affected by episodic loss of thick slabs and periglacial activity. Outcrops on main ridgelines erode slower than those on mountainside spur ridges because ridgelines are less likely to be covered by soil, which reduces the production rate of 10Be and increases the erosion rate of rock. Ridgeline outcrops erode slower than drainage basins in the Susquehanna and Potomac River watersheds, suggesting a landscape in disequilibrium. Erosion rates are more similar for outcrops meters to tens of meters apart than those at greater distances, yet semivariogram analysis suggests that outcrop erosion rates in the same physiographic province are similar even though they are hundreds of kilometers apart. This similarity may reflect underlying lithological and/or structural properties common to each physiographic province. Average 10Be-derived outcrop erosion rates are similar to denudation rates determined by other means (sediment flux, fission-track thermochronology, [U-Th]/He dating), indicating that the pace of landscape evolution in the central Appalachian Mountains is slow, and has been since post-Triassic rifting events.

INTRODUCTION

Appalachian landscape evolution has sparked over a century of discussion. An early theory of landscape evolution suggested that after being uplifted, peneplains were dissected by the rapid downcutting of streams, which eventually achieved a graded or equilibrium profile (Davis, 1899). Davis’s model persisted until Hack (1960) suggested that Appalachian landscapes were not the dissected remnants of uplifted plains, but rather resulted from the interaction of numerous driving forces including tectonics, erosion, climate, and physical properties of Earth materials. Contrary to Davis’s idea that landscapes evolved directionally over time, Hack proposed that landscapes only appear to preserve landforms. In reality, these landforms are continuously being eroded and uplifted in a dynamic equilibrium, where landscapes remain similar over the large scale but individual elements come and go over time as they are dismembered by erosion.

In the Appalachian Mountains, bedrock outcrops are often observed along ridgelines that define watershed boundaries. The existence of such ridges indicates that, at least for some time in the past, the landscape within the watersheds must have eroded more quickly than the rock ridges defining the watershed boundaries. This rapid downcutting may be initiated along structural weaknesses in the rock and perhaps by the process of headward stream capture (e.g., Clark, 1989; Gunnell and Harbor, 2010; Prince et al., 2010).

Around the globe, drainage basins appear to be eroding at least as quickly as or faster than bedrock outcrops in any region where both types of samples have been analyzed (Portenga and Bierman, 2011). Measured samples collected both from drainage basins and from individual bedrock outcrops on ridgelines provide important data for understanding landscape evolution through time, allowing one to contrast the rate of ridgeline lowering with that of basins as a whole. Comparing ridgeline erosion rates to basin erosion rates has the potential to determine whether relief is changing over time—a fundamental descriptor of landscape development. However, too little is known about bedrock outcrop erosion rates in the Appalachian Mountains to make such a comparison.

Traditional methods of measuring the pace of landscape change, such as chemical mass balances, sediment budgeting, and topographic measures of cliff or slope retreat over time, are difficult to apply or are unrepresentative at both the temporal and spatial scale of outcrop erosion (Saunders and Young, 1983). In contrast, cosmogenic methods are well suited for estimating outcrop erosion rates over millennial time scales (e.g., Nishiizumi et al., 1986), and the most widely used cosmogenic nuclide for erosion rate studies is 10Be measured in quartz (Portenga and Bierman, 2011).

Within the upper few meters of Earth’s surface, 10Be is created primarily by spallation nuclear reactions during which high-energy neutrons interact with oxygen in the mineral structure. The 10Be subsequently decays radioactively (t1/2 = 1.39 Ma; Chmeleff et al., 2010; Korschinek et al., 2010). The production of 10Be decreases exponentially with depth through Earth’s surface, such that at a depth of ∼2 m in rock, little 10Be is created through spallogenic reactions; muon-induced reactions continue to depths of tens of meters but at a much lower production rate. Thus, by sampling the uppermost portion of an outcrop and assuming steady and uniform erosion, the concentration of 10Be reflects the time required for material to pass through the uppermost several meters of rock and regolith (Lal, 1991); this is the basis of erosion rate calculations.

During the past decade, hundreds of 10Be measurements have been made on sediment samples collected from various streams and rivers near and within the central Appalachian Mountains, including drainage basins of different sizes in Shenandoah National Park (n = 37; Duxbury, 2009), the Susquehanna River Basin (n = 79; Reuter, 2005), and the Potomac River Basin (n = 62; Trodick, 2011). Paleo-erosion rates of contributing drainage basins to the New River in West Virginia were inferred from 10Be and 26Al concentrations in sediments deposited in caves (Granger et al., 1997). Basin-averaged erosion rates from these studies are low, with means averaging from 10 to 27 m m.y.−1 for sampled drainages in our study area. Incision rates of the New River (Ward et al., 2005) and of the Potomac and Susquehanna River (Reusser et al., 2006) were inferred from measurements of 10Be; such incision rates are reflective of only a single site, not erosion throughout each basin.

It is important to consider that basin-averaged erosion rates (Bierman and Steig, 1996; Brown et al., 1995; Granger et al., 1996) are not representative of the relatively small area occupied by ridgeline bedrock outcrops, nor are the processes by which rivers erode their basins the same as those by which outcrops lose mass. In fact, very few studies address the processes by which mass is lost from outcrops, though the effect of episodic removal of thick rock slabs from outcrops has been both modeled and measured (e.g., Bierman and Caffee, 2002; Lal, 1991; Small et al., 1997; Wakasa et al., 2006). In temperate climates, such as the central Appalachian Mountains, some bedrock erosion along ridgelines has been attributed not to contemporary processes but to periglacial activity during periods of colder climate, including enhanced freeze-thaw action and frost-heaving (Braun, 1989, 1993).

Prior to this study, 17 bedrock outcrop erosion rates had been published for the central Appalachian Mountains, including data from samples collected along ridges and summits in Shenandoah National Park (n = 5; Duxbury, 2009), the Susquehanna River Basin (n = 4; Reuter, 2005), and the Dolly Sods region of West Virginia (n = 8; Hancock and Kirwan, 2007). All three studies report low rates of outcrop erosion with means ranging from 4 to 7 m m.y.−1.

This study presents 72 new 10Be-based bedrock outcrop erosion rates from a variety of locations within the central Appalachian Mountains, specifically the Susquehanna River Basin (n = 26) and the Potomac River Basin (n = 46; Fig. 1); 62 of the 72 samples come from main ridgelines. The size of this new central Appalachian bedrock outcrop 10Be erosion rate data set and the spatial distribution of these data allow us to test for relationships among erosion rates and climatic and topographic parameters. By measuring the rate of bedrock outcrop erosion, we can better assess the processes influencing modeled erosion rates (e.g., block removal and periglacial activity) and discuss the relationship between the ridgeline and basin-averaged erosion rates, which is prerequisite to understanding large-scale landscape change on millennial time scales.

Geographic and Geologic Setting

The central Appalachian Mountains are a dominant physiographic feature inland of eastern North America’s Atlantic passive margin. This linear mountain chain extends 2500 km from the subpolar Canadian Maritime Provinces to humid, subtropical southern Georgia (Fig. 1, inset). The range is several hundred kilometers wide and generally steep, forested, and soil mantled, except along the highest ridgelines, where bedrock outcrops are common.

Five physiographic provinces are defined in and along the Appalachian Mountains (Fig. 1; Table 1). The undeformed sedimentary rock making up the Appalachian Plateau forms the western margin of the range. Further east, the highly deformed Valley and Ridge Province consists of a series of plunging anticlines and synclines of sedimentary rock. The Blue Ridge is a topographic feature held up in places by resistant units of quartz arenite and metamorphic quartzite extending from the Blue Ridge Escarpment in the south to just north of the Maryland-Pennsylvania border. The rolling Piedmont is underlain by high-grade metamorphic rocks. To the east, the low-lying Atlantic Coastal Plain exposes fluvial and shoreline sediments.

Over the Quaternary, continental ice sheets affected the northern Appalachian Mountains, advancing and retreating numerous times. At its farthest extent, ice covered the northern reaches of the Susquehanna River Basin (Fig. 1). Though the southern Appalachian Mountains were not directly affected by glaciation, the climate was cooler and drier during glaciations than it is today, and at least some of the ridgelines were affected by periglacial activity during cold phases (Braun, 1989).

METHODS

Field and Laboratory Methods

We located outcrops of quartz-rich lithologies based upon lithologic descriptions provided in bedrock geology maps downloaded from the U.S. Geological Survey (USGS) Geological Map Database (Fig. 1; Table 1; http://ngmdb.usgs.gov/) for Maryland, Pennsylvania, Virginia, and West Virginia. We cross-referenced names of sampling sites with gazetteers, topographic maps, and internet photos to assess the quality and accessibility of bedrock outcrops prior to field work.

Between two and four individual outcrop samples were collected from each of 26 sampling sites to test erosion rate variability at the outcrop scale; samples at each site were collected meters to tens of meters apart from one another (GSA Data Repository).1 We collected samples that fall into two categories: main ridgeline outcrops (n = 62) and spur ridge outcrops (n = 10). Main ridgeline samples are those from outcrops along the highest local topographic feature. Spur ridge outcrops are those from a ridge angling down and away from the main ridgeline (Figs. 2A–2C).

We sampled from two physiographic provinces in the Potomac River Basin and from four in the Susquehanna River Basin. Our samples come from the Appalachian Plateau (n = 5), the Valley and Ridge (n = 34), the Blue Ridge (n = 30), and the Piedmont (n = 3). The three Piedmont samples and five Appalachian Plateau samples are all within the Susquehanna River Basin, as no suitable bedrock outcrops were accessible in the Potomac River Basin during our field season.

Samples were returned to the University of Vermont, where they were processed to obtain purified quartz following the methods of Kohl and Nishiizumi (1992). The quartz from each sample and a known amount of 9Be carrier solution were digested in concentrated HF, and Be was subsequently separated from Fe, Al, Ti, and B. Specific quartz-preparation methods can be found at the University of Vermont Cosmogenic Nuclide Laboratory Web site (http://www.uvm.edu/cosmolab/?Page = methods.html).

The 10Be/9Be ratios were measured by accelerator mass spectrometry (AMS) at Lawrence Livermore National Laboratory in April 2010. All samples were normalized to standard 07KNSTD3110, with a reported 10Be/9Be ratio of 2.85 × 10−12 (Nishiizumi et al., 2007). The 10Be concentrations were derived from these 10Be/9Be ratios and used to calculate erosion rates using the CRONUS online cosmogenic erosion rate calculator (version 2.2; Balco et al., 2008). We used production rates corrected for latitude and elevation based on the scaling schemes of Lal (1991) and Stone (2000). Results are normalized to a high-latitude and sea-level 10Be production rate of 4.96 ± 0.43 atoms g−1 yr−1 (Balco et al., 2008). Errors reported in our analyses are 1σ AMS measurement errors and are propagated through the CRONUS calculations.

We analyzed bivariate relationships among sample erosion rates and physical and environmental parameters (Table 1; Table A1 [see footnote 1]) such as latitude (°N), elevation (m above sea level [asl]), relief (m in a 50 m radius of the outcrop), and mean annual precipitation (MAP, mm yr−1; Hijmans et al., 2005) and temperature (MAT, °C; Hijmans et al., 2005). We performed a multivariate standard least-squares regression; however, we recognize that our chosen environmental parameters are, in some cases, correlated with one another and thus performed a principal component analysis (PCA) to ensure that a line of best fit through our erosion rates was regressed through independent variables.

We used three statistical tests to compare the means of subgroups of data categories: analysis of variance (ANOVA), Tukey-Kramer honestly significant difference (HSD), and Students t-test. ANOVAs were used to determine the similarity of mean values between multiple groups. Tukey-Kramer HSD tests compared every possible pairing of groups from the ANOVA to determine which groups were statistically similar or dissimilar from one another. We performed these two methods for each set of categorical data (e.g., lithology) to determine whether the mean erosion rates of subgroups within each category (e.g., quartzite, sandstone, schist, etc.) were statistically similar. Just as we used an ANOVA to determine if the means of more than two groups of data were similar or dissimilar, we used a Student’s t-test to determine if means of two groups of data were similar or not (e.g., erosion rates from outcrops in the Potomac River Basin vs. those in the Susquehanna River Basin).

Spatial Statistics

We use several statistics to understand how erosion rates are spatially related. At the individual outcrop scale, we calculated the deviation of each sample to determine the spread of erosion rates at each sampling site. We then generalized the deviation at each site using the relative standard deviation (RSD) of erosion rates.

Different methods were needed to ascertain spatial patterns in our data across the entire field area. We tested the spatial autocorrelation of erosion rates using a semivariogram analysis developed and coded in Matlab (version 7.10.0, release 2010a). In the geostatistical literature, semivariance, γ(h), is used to describe spatial patterns between measured observations as a function of the separation distance. These patterns are usually described in terms of dissimilarity rather than similarity (or correlation). The spatial dissimilarity between observations separated by a distance h may be defined as:

The semivariogram plots the calculated variance between erosion rates from any two outcrops against the distance between those outcrops (Fig. 4A). Paired data are then separated into bins (Fig. 4B); the average variance for all paired data points in each bin is plotted as a single point along the y-axis. The resulting plot is known as the experimental semivariogram (Fig. 4B). This experimental semivariogram is best fit by a model semivariogram that describes the spatial structure (range of spatial autocorrelation) of the data defined by three model parameters—the nugget, sill, and range (Fig. 4C). The nugget is the projected discontinuity shown at the origin of the plot; it represents both the measured parameter error (in our case, error associated with collecting samples, measuring 10Be concentrations, and inferring erosion rates) as well as the spatial sources of variation at distances smaller than the shortest distance between samples (Journel and Huijbregts, 1978). For example, given no sampling or laboratory error, two erosion rate measurements taken from a fixed location at the top of the same outcrop should erode similarly, and the nugget would be 0. The range (referred to as the decorrelation distance) defines the distance at which the variable is no longer spatially autocorrelated. The semivariance associated with the model plateau is defined as the sill. It is possible for the variable in question to become spatially autocorrelated again at larger distances (i.e., where average bin values begin to increase consistently above the sill), resulting in a model semivariogram with multiple decorrelation distances.

Horizon Shielding Corrections

Samples from the top of an outcrop receive full cosmic-ray bombardment from the open sky, whereas those from sites that are shadowed or obstructed by other objects receive only a fraction of the total potential-cosmic ray bombardment. A shielding factor must be applied to samples collected from such sites before an erosion rate can be appropriately inferred (Dunne et al., 1999; Lal, 1991). Samples EPP43 and EPP44 were collected from a horizontal surface ∼1 m away from an ∼3-m-high vertical slab of rock at Seneca Rocks, in West Virginia. We used the methods of Dunne et al. (1999) to obtain an estimated shielding factor of ∼0.575 for these two samples, which we applied before using CRONUS to determine an erosion rate for each sample.

RESULTS

All 72 samples contained large amounts of 10Be, ranging from 6.69 × 104 to 3.23 × 106 atoms g−1. Such 10Be concentrations indicate that sampled outcrops integrate cosmic-ray dosing over more than 104 to nearly 106 yr. Concentrations of 10Be can be interpreted as outcrop exposure ages, assuming the outcrop erosion rate is negligible, or as outcrop erosion rates, assuming mass loss is constant and steady (Lal, 1991). Because field observations show signs of active mass loss from the sampled outcrops (e.g., granular disintegration, spalling of thin sheets of rock, loosening of rock slabs), we interpret the 10Be concentration data as rates of erosion. In reality, the episodic loss of slabs of rock from at least some outcrops means that measured 10Be concentrations reflect both the rate of erosion and the time since the last slab of rock peeled off the outcrop.

Bedrock outcrop erosion rates modeled from our measured 10Be concentrations range from 1.0 to 66 m m.y.−1 for samples from both main ridgelines and spur ridges. The average erosion rate for outcrops on main ridgelines from the entire field area is 9 ± 1 m m.y.−1 (n = 62; 1σ standard deviation; Table 1). The distribution of main ridgeline erosion rates is skewed to the left; the median rate of outcrop erosion is 6.4 m m.y.−1 (Fig. 3). Spur ridge outcrops erode at an average rate of 35 ± 2.5 m m.y.−1 (n = 10; Table 1) and have a median erosion rate of 27 m m.y.−1.

A Student’s t-test shows that outcrop erosion rates on spur ridges are significantly higher than those on main ridgelines (p < 0.01; Fig. 2D), implying the processes or the rates of processes controlling 10Be concentration differ between ridgelines and spur ridges. Because our interest is comparing the rate of ridgeline lowering to that of drainage basins as a whole, the following results and statistics are based solely on erosion rates from main ridgeline outcrops.

PCA created new variables (i.e., principal components), each explaining a different amount of the erosion rate variance (Tables A2 and A3 [see footnote 1]). When used in a multivariate standard least-squares analysis, these new independent variables describe less than 25% of erosion rate variability throughout our field site (R2 = 0.22, p = 0.01; Table 2).

Spatial Variance of Erosion Rates

Erosion rates of ridgeline outcrops are similar between the two basins (p = 0.53; Table 3; Fig. 5E). The range of bedrock outcrop erosion rates in the Potomac River Basin (1.0–40 m m.y.−1; n = 40) is broader than that of bedrock outcrops in the Susquehanna River Basin (1.8–28 m m.y.−1; n = 22), likely due to a larger sample population in the Potomac data set; the median outcrop erosion rates are also similar (6.1 and 6.9 m m.y.−1, respectively) between the river basins (Fig. 5E; Table 3).

Outcrop erosion rates from the same physiographic provinces (i.e., Blue Ridge, Valley and Ridge) are also similar, even when spread across two separate drainage basins (Figs. 5C and 5F; Table 3). No comparison can be made for bedrock outcrops situated in the Appalachian Plateau or Piedmont Provinces because all samples for these provinces were collected within the Susquehanna River Basin. An ANOVA indicates statistically significant differences in the mean erosion rates from each province (p < 0.01; Fig. 5C), and the results from a Tukey-Kramer HSD analysis show that erosion rates from the Valley and Ridge are significantly higher than those from the Appalachian Plateau and those from the Blue Ridge (p < 0.01; Fig. 5D); otherwise, erosion rates from all other pairs of provinces are indistinguishable (Table 3).

RSDs from each sampling site cover a wide range (3%–98%; Table 1), with an average of 37% for 26 sampling sites. Erosion rates are more variable throughout our entire field area (108,000 km2) than at individual sampling sites, as indicated by a large relative standard deviation based on all sampled outcrops (RSD = 93%). While the range of outcrop erosion rates through the field area is large (1.0–66 m m.y.−1), the average deviation of any erosion rate from the mean of its sampling site is low (2.6 m m.y.−1; Table 1).

Ridgeline samples (n = 62) yielded a total of 1892 unique distances between any two sampling sites throughout the entire field area. These pairs of data were grouped into ten bins of equal population size, and the average semivariance for each bin was calculated (Fig. 4B). The experimental semivariogram is best fit by an exponential model with two spatial ranges: one at 68 km and the other at 196 km (Fig. 4C). The nugget suggests that samples separated by a distance smaller than the closest two outcrops we sampled (∼1 m) are more likely to produce erosion rates that vary up to 1 m m.y.−1 from the average, a projected value not dissimilar to the observed average deviation of 2.6 m m.y.−1.

DISCUSSION

The 10Be concentrations measured in samples from ridgeline outcrops in the central Appalachian Mountains indicate low erosion rates (Fig. 3), best represented by the median erosion rate of 6 m m.y.−1. Assuming steady erosion, these rates suggest that 10Be measurements integrate over 104–106 yr of erosional history, which is the time it would take to remove several meters of rock from the sampled outcrops. Although erosion rates as high as a few tens of meters per million years exist within our data set, the majority of outcrops suggest that ridgelines within our study area are eroding slowly, and the time-averaged erosion rate determined by our 62 main ridgeline samples thus sets the pace for ridgeline lowering.

Influence of Outcrop Exposure History on Erosion Rates

We infer outcrop erosion rates from a measured quantity of 10Be; however, we cannot accurately describe an outcrop’s exact exposure history or quantify stochastic erosion processes that affect the interpretation of nuclide concentrations. A key assumption in the cosmogenic erosion rate methodology is that a sampled bedrock outcrop surface, once exposed to cosmic rays, continues to be bombarded at a constant rate while material is gradually removed from the outcrop. Sometimes, however, mass is often removed from an outcrop by the loss of large blocks or slabs (e.g., Bierman and Caffee, 2002; Small et al., 1997) or by landsliding (e.g., Niemi et al., 2005). When block-removal occurs and a sample is collected from a newly exposed rock surface, the inferred erosion rate will overestimate the time-averaged erosion rate; conversely, when a sample is collected from an outcrop that fails soon after the sample was removed, the inferred erosion rate may be underestimated (Lal, 1991; Small et al., 1997).

Small amounts of soil cover or colluvium can increase the rate at which bedrock erodes and reduce the production rate of 10Be by absorbing some cosmic rays (Braun, 1989; Heimsath et al., 1997, 1999). Such cover creates conditions favorable for accelerated water-rock interactions, speeding the rate of erosion by processes including chemical weathering, freeze-thaw, frost-heave, and in periglacial environments, solifluction (Braun, 1989, 1993; Eaton et al., 2003; Heimsath et al., 1999). Some of these erosion processes are particularly active on periglacial hillslopes, where near-surface temperatures fluctuate around the freezing point (Delunel et al., 2010; Hales and Roering, 2007), a common occurrence in parts of the Potomac and Susquehanna River Basins during glacial periods (Peel et al., 2007).

Compared to main ridgeline outcrops, spur ridge outcrops (at lower elevations on hillslopes) are more susceptible to being covered by soil, sediment, and colluvium transported downslope from above. Intermittent burial of spur-ridge outcrops leads to conditions that both hasten rock erosion and also attenuate the cosmic-ray flux, reducing the 10Be production rate, and thus the nuclide concentration in spur ridge samples.

Physical and Spatial Variables Influencing Outcrop Erosion Rates

Bivariate correlations between erosion rates and various parameters may be indicative of processes affecting the rate at which rock erodes and are useful when interpreting observations on a small spatial scale (Portenga and Bierman, 2011); however, bivariate correlations can be strongly affected by autocorrelation of variables, making causal attribution uncertain. Because such bivariate relationships are not independent of one another, interactions between variables are likely combined to affect erosion rates in ways we do not yet understand.

The bivariate correlation between erosion rate and relief is the strongest in our data set (Table 2) and has been observed in landscapes around the world (Montgomery and Brandon, 2002; Portenga and Bierman, 2011). We observe a weak bivariate correlation between erosion rate and elevation (Table 2). A similar relationship has been observed in regions with annual temperatures that fluctuate around the freezing point, such as the Appalachian Mountains, and this has been attributed to a higher likelihood of water-fed frost cracking (Hales and Roering, 2007; Portenga and Bierman, 2011).

Multivariate least-squares regression analyses of environmental variables only account for 22% of erosion rate variability (Table 2); therefore, unquantified rock properties (e.g., cementation, porosity, fracture density) and temporal factors such as response to base-level change, drainage migration, and local uplift may play significant roles in controlling bedrock outcrop erosion rates. For example, structural deformation of rock, and the resulting foliation and jointing, likely affects overall rock strength, consistent with our observation that outcrop erosion rates in the highly deformed Valley and Ridge Province are significantly higher than those in the undeformed Appalachian Plateau Province (Figs. 1 and 5C). Other rock properties such as cementation and rock strength may explain why we observe lower erosion rates in weathering-resistant quartzite outcrops of the Blue Ridge Province compared to the sandstone outcrops of the Valley and Ridge Province (Figs. 1 and 5C). Furthermore, stream capture separates sandstone-capped ridges, relicts of the retreating passive-margin escarpment, from the continental divide as it migrates landward through the Valley and Ridge, effectively lowering the base level of streams on all sides of these ridges (Gunnell and Harbor, 2010), possibly contributing to their higher rates of erosion.

We observe a decrease in erosion rates with an increase in latitude (Table 2), though it is not clear why. Some studies suggest that erosion rates increase in regions closer to previous periglacial activity (Braun, 1989; Hales and Roering, 2007); however, on a global scale, outcrops in polar climates erode more slowly than those in temperate climates (Portenga and Bierman, 2011), leading one to expect lower outcrop erosion rates with increasing latitude. We investigated whether slower-eroding lithologies (i.e., schist, arenite, quartzite) were grouped at higher latitudes in our field area, but they are not; rather, sandstone outcrops have the largest latitudinal range of any lithology we sampled (∼38°N–41°N). Among sandstone outcrops, there is a significant negative correlation between erosion rates and increasing latitude (n = 28; R2 = 0.39; p < 0.01). No other lithology has as large a latitudinal spread, and none indicates any relationship between erosion rates and latitude.

During the Pleistocene, streams flowing toward the Atlantic Ocean broke through the Blue Ridge in Virginia, initiating a southwestern migration of the continental divide through the Valley and Ridge as the Susquehanna, Potomac, and James Rivers pirated stream catchments that had previously drained south to the Gulf of Mexico (e.g., Erickson and Harbor, 1998; Harbor et al., 2005; Naeser et al., 2001; Prince et al., 2010). Contemporaneously, the source of sediment in Mid-Atlantic offshore basins shifted to the south (e.g., Gunnell and Harbor, 2010; Poag and Sevon, 1989). While it is possible that our observed southerly increase in outcrop erosion rates reflects the southerly shift in sediment deposition, it is not likely, as basin-averaged erosion rates estimated with 10Be from the Susquehanna and Potomac River Basins do not follow this trend but rather increase at higher latitudes (Reuter, 2005; Trodick, 2011).

The variability in erosion rates increases with distance between outcrop samples. For example, although the RSD calculated from all 62 main ridgeline outcrops is high (93%), the average RSD at our 26 individual sampling sites is much lower (37%), indicating that the erosion rate variability decreases when samples are separated by distances of only tens of meters, a finding consistent with the semivariogram analysis (Fig. 6). The range of sampling site RSDs is wide (3%–98%; Table 1), but because most erosion rates are low (median = 6 m m.y.−1), the higher RSD values of some sampling sites (e.g., Chimney Rock in Catoctin Mountain Park, Buzzards Rock, Turtle Rocks) reflect erosion rate differences of only ∼2–4 m m.y.−1. Furthermore, the average deviation from the average erosion rate at any sampling site is only 2.6 m m.y.−1; outcrops in the immediate vicinity of one another usually do not have very different erosion rates when considered in the absolute sense. The implications of this finding are significant in that a single outcrop sample represents reasonably well the erosion rate of an entire sampling site, though the overall erosion rate of a large region is best represented by a large sample population.

We infer—based on both isotope data and field observations—that samples from sampling sites with low RSDs (e.g., Panther Rocks, Duncan Knob, Miller Rock) have lost mass by steady granular disintegration or the peeling of thin sheets rather than by shedding thick slabs. Sites with high RSDs (e.g., Buzzards Rock, Turtle Rocks, Rock Ridge) most likely include a sample from a surface exposed after block failure or from one that is likely to experience a loss of mass by block failure in the near future.

Semivariogram results show that our measured erosion rates are spatially autocorrelated at two distances: 68 km and 196 km (Fig. 6). The 68 km decorrelation distance is large enough for erosion rates from each sampling site to be correlated to those from the next-closest sampling sites. Similar to the observed average deviation, the 68 km decorrelation distance reflects observations showing outcrops eroding more similarly to other nearby outcrops than to those at greater distances. The second decorrelation distance suggests that erosion rates are also correlated at a distance of 196 km, though the interpretation is less clear. At 196 km, observations suggest that all measured erosion rates separated by this distance are more correlated to each other than erosion rates at greater separation distances. This distance likely reflects a correlation between erosion rates of outcrops in a physiographic province from one drainage basin to those in the same physiographic province in the other drainage basin (Fig. 6), an observation consistent with the results of Student’s t-tests showing similarity among the means of these rates (Fig. 5F). Correlations between erosionrates in the same physiographic province, no matter the distance between them, may be due to underlying structural, lithological, or mechanical properties of the rock within a province such as the specific mineralogy, type of cementation, dip of strata, joint and fracture spacing in the outcrop, or rock hardness.

Comparing Erosion Rates to Other Studies

Inferred outcrop erosion rates from three previous studies in the central Appalachian Mountains (Duxbury, 2009; Hancock and Kirwan, 2007; Reuter, 2005) are consistent with our conclusion that the landscape is slowly changing (Fig. 7). An ANOVA reveals no significant difference among the means of sample populations from these studies (p = 0.44), and results from a Tukey-Kramer HSD analysis indicate equality between the means of each sample population (p > 0.42; Fig. 7). Average bedrock outcrop erosion rates in the central Appalachian Mountains (9 m m.y.−1) are also consistent with bedrock outcrop erosion rates in quiescent tectonic settings around the world (n = 299) inferred from cosmogenic 10Be (12 m m.y.−1; p = 0.18; Fig. 8; Portenga and Bierman, 2011).

Basin-averaged erosion rates from 178 central Appalachian watersheds with basin-average slopes ranging from ∼1° to 24° result in a mean watershed erosion rate of 15 m.y.−1, which is higher than the average erosion rate estimated from exposed bedrock outcrops (9 m m.y.−1) in the central Appalachian Mountains (Duxbury, 2009; Reuter, 2005; Trodick, 2011; Fig. 9). Within the Potomac River Basin, outcrop erosion rates (9.6 m m.y.−1; n = 45) are just slightly less than those of drainage basins (10 m m.y.−1; n = 99; p = 0.01) determined from multiple other studies (e.g., Duxbury, 2009; Trodick, 2011). Because our focus is identifying erosion patterns within the Potomac River Basin, only basins draining westward from Shenandoah National Park into the Potomac River Basin were used for this statistical comparison (Duxbury, 2009). In contrast, in the Susquehanna River Basin, the average drainage basin erosion rate (20 m m.y.−1; n = 79; Reuter, 2005) is more than twice that of bedrock outcrops (8 m m.y.−1; n = 26; p < 0.01; Fig. 9).

At face value, the disparity between basin-averaged and outcrop erosion rates suggests that relief is increasing in both drainage basins, though at a much greater rate in the Susquehanna River Basin (Fig. 9). There are, however, other plausible explanations for our observations. Periglacial activity may have accelerated mass loss from basin slopes, introducing once-shielded and less-dosed material into the region’s streams while outcrops were less affected. Another possibility is that basin-averaged erosion rates may reflect the influence of deep-seated mass movements, perhaps triggered by torrential hurricane rains, delivering less-dosed material to the channels (Adams and Spotila, 2005; Eaton et al., 2003; Mills, 1981; Niemi et al., 2005; Tucker and Bras, 2000). Conversely, basin-averaged erosion rates may reflect transients in landscape behavior such as rapid downcutting following stream-capture events and effective base-level fall, ideas initially envisioned by Davis (1899). Examples of transient landscape conditions could be associated with the southwest migration of the continental divide in the central Appalachian Mountains past the Blue Ridge and into the Valley and Ridge (e.g., Clark, 1989; Erickson and Harbor, 1998; Harbor et al., 2005; Naeser et al., 2001; Prince et al., 2010), although such transients may not be apparent from our outcrop erosion data set alone.

The similarly low erosion and denudation rates from thermochronologic, basin-averaged cosmogenic, and sediment flux studies confirm a history of slow landscape lowering in the central Appalachian Mountains since post-Alleghenian rifting events and most likely reflect isostatically driven rebound responding to mass loss by erosion. Such similarity between erosion rates on cosmogenic and thermochronologic time scales is not uncommon. Similar observations have been made in Namibia (Cockburn et al., 2000), the California Sierra Nevada (Granger et al., 2001), and Sri Lanka (von Blanckenburg et al., 2004). The offset between basin-scale and ridgeline outcrop cosmogenic erosion rate estimates is consistent with increasing relief over the later Pleistocene. Conversely, it could be an artifact of the way in which ridgeline outcrops erode: While erosion from above is slow, erosion from the margins is more rapid, eventually destroying ridgelines from the side. Such a scenario suggests that stable ridgelines themselves are transient landscape features and would resolve the disparity between erosion rate estimates at different time and spatial scales.

CONCLUSIONS

The central Appalachian Mountains include slowly changing landscapes in the Potomac and Susquehanna River Basins. The highest ridgelines in the area are eroding slowly (on average 9 m m.y.−1) and have been for at least tens to hundreds of thousands of years. Though there is spatial variability in bedrock outcrop erosion rates along ridgelines, the median erosion rate (6 m m.y.−1) shows that mass in this region is not quickly removed from exposed outcrops.

Outcrop erosion rates vary. Single environmental parameters, either alone or considered together, explain only a small part (22%) of the erosion rate variability we measured in the central Appalachians; much of the unexplained variability is likely related to specific properties of each rock outcrop, including joint spacing, rock strength, mineralogy, and porosity. The factors, whether environmental or physical, that control erosion rates of exposed rock remain uncertain.

Average erosion rates from cosmogenic 10Be in bedrock outcrops on main ridgelines (this study and others) are consistent with data from other methods integrating over longer time windows and indicate that the central Appalachian Mountains have been wearing down at rates no more than a few tens of meters per million years since post-Triassic rifting events. The disparity between outcrop- and basin-scale rates of erosion is consistent with an increase in relief over the cosmogenic time scale (tens to hundreds of thousands of years) or a process by which stable ridgelines are eventually destroyed by erosion from their perimeters.

We thank R. Harriett (Harpers Ferry National Historical Park), B. Loncoski (Catoctin Mountain Park), B. Norden (Maryland Department of Natural Resources), T. Collins (George Washington National Forest), L. Tracey (Monongahela National Forest), S. Summers (West Virginia Division of Natural Resources), and G. Blackmer (Pennsylvania Department of Conservation and Natural Resources) for assistance in obtaining permits and C. Trodick Jr. for his help in collecting samples. We also thank Gregory Hancock and James Spotila for their thoughtful and constructive comments and guidance throughout the review process. This work was supported by National Science Foundation grant EAR-310208 and U.S. Geological Survey grant 08ERSA0582 and was performed in part under the auspices of the U.S. Department of Energy at Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344.

Results from a semivariogram analysis of spatial autocorrelation within the outcrop erosion rate data. (A) The variance between erosion rates from any given outcrop sample and all other outcrop samples plotted against the separation distance between any two samples. (B) Variance values associated with paired data are grouped into ten bins of equal population and an experimental semivariogram is produced by plotting the average variance for each bin, represented by the white plus signs; the dotted lines represent the 95% confidence interval of the average semivariance values. (C) An exponential model semivariogram best fits the experimental semivariogram, illustrating the spatial structure of the data using the nugget, sill, and range. This approach often yields one decorrelation distance; however, in our case, erosion rates are spatially autocorrelated at two distances.

Results from a semivariogram analysis of spatial autocorrelation within the outcrop erosion rate data. (A) The variance between erosion rates from any given outcrop sample and all other outcrop samples plotted against the separation distance between any two samples. (B) Variance values associated with paired data are grouped into ten bins of equal population and an experimental semivariogram is produced by plotting the average variance for each bin, represented by the white plus signs; the dotted lines represent the 95% confidence interval of the average semivariance values. (C) An exponential model semivariogram best fits the experimental semivariogram, illustrating the spatial structure of the data using the nugget, sill, and range. This approach often yields one decorrelation distance; however, in our case, erosion rates are spatially autocorrelated at two distances.

Box and whisker plots of outcrop erosion rates used in analysis of variance (ANOVA). The ends of the whiskers denote the range of erosion rates in each category, while the top, middle, and bottom of each box mark the 75th, 50th, and 25th percentiles, respectively. Gray dots represent individual erosion rate samples. (A) Climate zone, as classified by the Köppen-Geiger Classification system (Peel et al., 2007): Temperate climates have an annual high temperature >10 ° C and an annual low temperature >0 ° C but <10 ° C; cold climates have an annual high temperature >10 ° C and an annual low temperature of ? 0 °C; Cfa—Temperate: hot summer without dry season; Dfa—Cold: hot summer without dry season; Dfb—Cold: warm summer without dry season. (B) Outcrop lithology. Samples from quartz vein, phyllite, and conglomerate outcrops are not included because their sample populations are too small (n = 2). (C) Physiographic provinces of the central Appalachian Mountains from which samples were collected. (D) Results of paired analyses of Tukey-Kramer honestly significant difference (HSD) analyses of climate zones, lithologies, and physiographic provinces. (E) Outcrop erosion rates for the Susquehanna and Potomac River Basins. A Student’s t-test indicates similarity between the two sample populations (p = 0.53). (F) Outcrop erosion rates from the Blue Ridge and Valley and Ridge Provinces of both the Potomac and Susquehanna River Basins. Erosion rates are not statistically different in either physiographic province.

Box and whisker plots of outcrop erosion rates used in analysis of variance (ANOVA). The ends of the whiskers denote the range of erosion rates in each category, while the top, middle, and bottom of each box mark the 75th, 50th, and 25th percentiles, respectively. Gray dots represent individual erosion rate samples. (A) Climate zone, as classified by the Köppen-Geiger Classification system (Peel et al., 2007): Temperate climates have an annual high temperature >10 ° C and an annual low temperature >0 ° C but <10 ° C; cold climates have an annual high temperature >10 ° C and an annual low temperature of ? 0 °C; Cfa—Temperate: hot summer without dry season; Dfa—Cold: hot summer without dry season; Dfb—Cold: warm summer without dry season. (B) Outcrop lithology. Samples from quartz vein, phyllite, and conglomerate outcrops are not included because their sample populations are too small (n = 2). (C) Physiographic provinces of the central Appalachian Mountains from which samples were collected. (D) Results of paired analyses of Tukey-Kramer honestly significant difference (HSD) analyses of climate zones, lithologies, and physiographic provinces. (E) Outcrop erosion rates for the Susquehanna and Potomac River Basins. A Student’s t-test indicates similarity between the two sample populations (p = 0.53). (F) Outcrop erosion rates from the Blue Ridge and Valley and Ridge Provinces of both the Potomac and Susquehanna River Basins. Erosion rates are not statistically different in either physiographic province.

Outcrop erosion rates are spatially autocorrelated at two distances in our study area. (A) Map of the study area illustrating the 68 km decorrelation distance determined from semivariogram analyses (Fig. 4). Light-gray, dashed circles represent a 68 km radius around each sampled outcrop, while the darker-gray line marks the perimeter of the overall area of spatial autocorrelation. The 65 km distance represents the distance from one sampling site to the next. (B) Map of the study area illustrating the 196 km decorrelation distance between outcrops from the Blue Ridge Province. (C) Map of the study area illustrating the 196 km decorrelation distance between outcrops from the Valley and Ridge Province. The 196 km distance indicates that erosion rates from outcrops from one physiographic province in one basin are spatially autocorrelated to those from the same physiographic province of the other basin.

Outcrop erosion rates are spatially autocorrelated at two distances in our study area. (A) Map of the study area illustrating the 68 km decorrelation distance determined from semivariogram analyses (Fig. 4). Light-gray, dashed circles represent a 68 km radius around each sampled outcrop, while the darker-gray line marks the perimeter of the overall area of spatial autocorrelation. The 65 km distance represents the distance from one sampling site to the next. (B) Map of the study area illustrating the 196 km decorrelation distance between outcrops from the Blue Ridge Province. (C) Map of the study area illustrating the 196 km decorrelation distance between outcrops from the Valley and Ridge Province. The 196 km distance indicates that erosion rates from outcrops from one physiographic province in one basin are spatially autocorrelated to those from the same physiographic province of the other basin.

Box and whisker plots of outcrop erosion rates used in analysis of variance (ANOVA) comparing average erosion rates from bedrock outcrops in the central Appalachian region, including those from Shenandoah National Park (Duxbury, 2009), Dolly Sods in West Virginia (Hancock and Kirwan, 2007), and sites in the Susquehanna River Basin (Reuter, 2005). Outcrop locations for all studies can be found in Figure 1. Results and p values of a Tukey-Kramer honestly significant difference (HSD) analyses are shown in the stair-step chart. The ends of the whiskers mark the range of erosion rates in each category, while the top, middle, and bottom of each box mark the 75th, 50th, and 25th percentiles, respectively. Gray dots represent individual erosion rate samples.

Box and whisker plots of outcrop erosion rates used in analysis of variance (ANOVA) comparing average erosion rates from bedrock outcrops in the central Appalachian region, including those from Shenandoah National Park (Duxbury, 2009), Dolly Sods in West Virginia (Hancock and Kirwan, 2007), and sites in the Susquehanna River Basin (Reuter, 2005). Outcrop locations for all studies can be found in Figure 1. Results and p values of a Tukey-Kramer honestly significant difference (HSD) analyses are shown in the stair-step chart. The ends of the whiskers mark the range of erosion rates in each category, while the top, middle, and bottom of each box mark the 75th, 50th, and 25th percentiles, respectively. Gray dots represent individual erosion rate samples.

(A) Data used in a Student’s t-test comparing the means of outcrop erosion rates in quiescent tectonic settings from around the world (n = 299; Portenga and Bierman, 2011) and in the central Appalachian Mountains. (B) Exceedance probability of global 10Be erosion rates (light-gray line) shows a wider distribution than samples from this study (dark-gray line). (C) Histograms of cosmogenic 10Be erosion rates around the world (light-gray bars) and this study (inset dark-gray bars), both showing a skewed distribution toward low erosion rates.

(A) Data used in a Student’s t-test comparing the means of outcrop erosion rates in quiescent tectonic settings from around the world (n = 299; Portenga and Bierman, 2011) and in the central Appalachian Mountains. (B) Exceedance probability of global 10Be erosion rates (light-gray line) shows a wider distribution than samples from this study (dark-gray line). (C) Histograms of cosmogenic 10Be erosion rates around the world (light-gray bars) and this study (inset dark-gray bars), both showing a skewed distribution toward low erosion rates.

Data used in a Student’s t-test analysis comparing the means of basin-averaged and outcrop erosion rates in the Potomac and Susquehanna River Basins. Both analyses show basin-averaged erosion rates in each river system to be significantly higher than outcrop erosion rates. Samples come from this study and numerous others (Duxbury, 2009; Reuter, 2005; Trodick, 2011). Only westward-draining basins from the Duxbury (2009) study in Shenandoah National Park were used for this analysis; eastward-draining basins are not part of the Potomac River drainage system.

Data used in a Student’s t-test analysis comparing the means of basin-averaged and outcrop erosion rates in the Potomac and Susquehanna River Basins. Both analyses show basin-averaged erosion rates in each river system to be significantly higher than outcrop erosion rates. Samples come from this study and numerous others (Duxbury, 2009; Reuter, 2005; Trodick, 2011). Only westward-draining basins from the Duxbury (2009) study in Shenandoah National Park were used for this analysis; eastward-draining basins are not part of the Potomac River drainage system.